Automatic Use Case and Class Diagram Generation |
Author(s): |
| Sai Sruthi , CBIT |
Keywords: |
| UML Diagrams, Heuristic Rules, NLP Techniques |
Abstract |
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Unified Modeling Language (UML) is the most popular modeling language for analysis, design and development of the software system. There has been a lot of research interest in generating these UML models, especially Use Case and Class diagrams, automatically from Natural Language requirements. System specifications and facilitating systems development is best described by use case modelling which is referred as an important requirements engineering technique. Generating Use Case models and class diagrams from linguistic representations of system requirements as a source of information is a challenging task and is also considered as a new field. We try to solve the problem of extracting the required elements that are needed to automatically generate Use Case diagrams and class diagrams from specification documents which are written in common natural language. Therefore, we are developing an automated system which employs the Natural Language Processing (NLP) techniques to parse specifications syntactically based on a predefined set of heuristic rules. Furthermore, our system incorporates the capability of analyzing and understanding the English text as a semantic unit to infer some important linguistic features such as reference, comparing and additive cohesive devices. The extracted information is then mapped into actors and use cases, which are the basic elements of Use Case diagrams. The interest in class diagrams can be attributed to the fact that classes represent the abstractions present in the system to be developed. However, automated generation of UML class diagrams is a challenging task as it involves lot of pre-processing or manual intervention at times. The knowledge extracted for the Use Case diagrams is used to derive class diagrams. Our approach has generated similar class diagrams as reported in earlier works based on linguistic analysis with either annotation or manual intervention. |
Other Details |
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Paper ID: IJSRDV6I100206 Published in: Volume : 6, Issue : 10 Publication Date: 01/01/2019 Page(s): 452-457 |
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